{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "# Synthetically generated datasets from TODS\n",
    "\n",
    "- Source: https://github.com/datamllab/tods/tree/benchmark/benchmark/synthetic\n",
    "- Description: https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/ec5decca5ed3d6b8079e2e7e7bacc9f2-Paper-round1.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from typing import List\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import matplotlib.pyplot as plt\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "from timeeval import DatasetManager\n",
    "from timeeval.datasets import Datasets, DatasetAnalyzer, DatasetRecord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "plt.rcParams[\"figure.figsize\"] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_datasets(it):\n",
    "    if it == \"univariate\":\n",
    "        return [\n",
    "            f for f in (source_folder / \"unidataset\").iterdir()\n",
    "            if f.name.startswith(\"point_\") or f.name.startswith(\"collective_\")\n",
    "        ]\n",
    "    else:\n",
    "        return [\n",
    "            f for f in (source_folder / \"multidataset\").iterdir()\n",
    "        ]\n",
    "\n",
    "def prepare_dataset(dataset_path: Path) -> pd.DataFrame:\n",
    "    df = pd.read_csv(dataset_path)\n",
    "    df.index.name = \"timestamp\"\n",
    "    df = df.rename(columns={\"anomaly\": \"is_anomaly\"})\n",
    "    df = df.reset_index()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"TODS-synthetic\"\n",
    "source_folder = Path(data_raw_folder) / \"TODS-synthetic\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "print(f\"Looking for source datasets in {Path(source_folder).absolute()} and\\nsaving processed datasets in {Path(target_folder).absolute()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "# shared by all datasets\n",
    "dataset_type = \"synthetic\"\n",
    "datetime_index = False\n",
    "split_at = None\n",
    "train_is_normal = False\n",
    "train_type = \"unsupervised\"\n",
    "\n",
    "dm = DatasetManager(target_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Created directories /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic\n",
      "> Processing source dataset 0: point_global_0.1.csv\n",
      "  written dataset 0 to disk\n",
      "  analyzed test dataset 0\n",
      "... processed source dataset 0: point_global_0.1.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_global_0.1.test.csv\n",
      "> Processing source dataset 1: collective_global_0.15.csv\n",
      "  written dataset 1 to disk\n",
      "  analyzed test dataset 1\n",
      "... processed source dataset 1: collective_global_0.15.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_global_0.15.test.csv\n",
      "> Processing source dataset 2: collective_seasonal_0.05.csv\n",
      "  written dataset 2 to disk\n",
      "  analyzed test dataset 2\n",
      "... processed source dataset 2: collective_seasonal_0.05.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_seasonal_0.05.test.csv\n",
      "> Processing source dataset 3: point_contextual_0.1.csv\n",
      "  written dataset 3 to disk\n",
      "  analyzed test dataset 3\n",
      "... processed source dataset 3: point_contextual_0.1.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_contextual_0.1.test.csv\n",
      "> Processing source dataset 4: collective_trend_0.1.csv\n",
      "  written dataset 4 to disk\n",
      "  analyzed test dataset 4\n",
      "... processed source dataset 4: collective_trend_0.1.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_trend_0.1.test.csv\n",
      "> Processing source dataset 5: point_contextual_0.2.csv\n",
      "  written dataset 5 to disk\n",
      "  analyzed test dataset 5\n",
      "... processed source dataset 5: point_contextual_0.2.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_contextual_0.2.test.csv\n",
      "> Processing source dataset 6: point_global_0.2.csv\n",
      "  written dataset 6 to disk\n",
      "  analyzed test dataset 6\n",
      "... processed source dataset 6: point_global_0.2.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_global_0.2.test.csv\n",
      "> Processing source dataset 7: collective_seasonal_0.2.csv\n",
      "  written dataset 7 to disk\n",
      "  analyzed test dataset 7\n",
      "... processed source dataset 7: collective_seasonal_0.2.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_seasonal_0.2.test.csv\n",
      "> Processing source dataset 8: collective_trend_0.2.csv\n",
      "  written dataset 8 to disk\n",
      "  analyzed test dataset 8\n",
      "... processed source dataset 8: collective_trend_0.2.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_trend_0.2.test.csv\n",
      "> Processing source dataset 9: point_global_0.05.csv\n",
      "  written dataset 9 to disk\n",
      "  analyzed test dataset 9\n",
      "... processed source dataset 9: point_global_0.05.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_global_0.05.test.csv\n",
      "> Processing source dataset 10: point_global_0.15.csv\n",
      "  written dataset 10 to disk\n",
      "  analyzed test dataset 10\n",
      "... processed source dataset 10: point_global_0.15.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_global_0.15.test.csv\n",
      "> Processing source dataset 11: point_contextual_0.15.csv\n",
      "  written dataset 11 to disk\n",
      "  analyzed test dataset 11\n",
      "... processed source dataset 11: point_contextual_0.15.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_contextual_0.15.test.csv\n",
      "> Processing source dataset 12: collective_trend_0.15.csv\n",
      "  written dataset 12 to disk\n",
      "  analyzed test dataset 12\n",
      "... processed source dataset 12: collective_trend_0.15.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_trend_0.15.test.csv\n",
      "> Processing source dataset 13: collective_global_0.2.csv\n",
      "  written dataset 13 to disk\n",
      "  analyzed test dataset 13\n",
      "... processed source dataset 13: collective_global_0.2.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_global_0.2.test.csv\n",
      "> Processing source dataset 14: collective_trend_0.05.csv\n",
      "  written dataset 14 to disk\n",
      "  analyzed test dataset 14\n",
      "... processed source dataset 14: collective_trend_0.05.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_trend_0.05.test.csv\n",
      "> Processing source dataset 15: collective_seasonal_0.15.csv\n",
      "  written dataset 15 to disk\n",
      "  analyzed test dataset 15\n",
      "... processed source dataset 15: collective_seasonal_0.15.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_seasonal_0.15.test.csv\n",
      "> Processing source dataset 16: point_contextual_0.05.csv\n",
      "  written dataset 16 to disk\n",
      "  analyzed test dataset 16\n",
      "... processed source dataset 16: point_contextual_0.05.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/point_contextual_0.05.test.csv\n",
      "> Processing source dataset 17: collective_global_0.1.csv\n",
      "  written dataset 17 to disk\n",
      "  analyzed test dataset 17\n",
      "... processed source dataset 17: collective_global_0.1.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_global_0.1.test.csv\n",
      "> Processing source dataset 18: collective_seasonal_0.1.csv\n",
      "  written dataset 18 to disk\n",
      "  analyzed test dataset 18\n",
      "... processed source dataset 18: collective_seasonal_0.1.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_seasonal_0.1.test.csv\n",
      "> Processing source dataset 19: collective_global_0.05.csv\n",
      "  written dataset 19 to disk\n",
      "  analyzed test dataset 19\n",
      "... processed source dataset 19: collective_global_0.05.csv -> /home/projects/akita/data/benchmark-data/data-processed/univariate/TODS-synthetic/collective_global_0.05.test.csv\n",
      "Created directories /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic\n",
      "> Processing source dataset 0: 012.csv\n",
      "  written dataset 0 to disk\n",
      "  analyzed test dataset 0\n",
      "... processed source dataset 0: 012.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/012.test.csv\n",
      "> Processing source dataset 1: 2.csv\n",
      "  written dataset 1 to disk\n",
      "  analyzed test dataset 1\n",
      "... processed source dataset 1: 2.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/2.test.csv\n",
      "> Processing source dataset 2: 4.csv\n",
      "  written dataset 2 to disk\n",
      "  analyzed test dataset 2\n",
      "... processed source dataset 2: 4.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/4.test.csv\n",
      "> Processing source dataset 3: 34.csv\n",
      "  written dataset 3 to disk\n",
      "  analyzed test dataset 3\n",
      "... processed source dataset 3: 34.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/34.test.csv\n",
      "> Processing source dataset 4: 01234.csv\n",
      "  written dataset 4 to disk\n",
      "  analyzed test dataset 4\n",
      "... processed source dataset 4: 01234.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/01234.test.csv\n",
      "> Processing source dataset 5: 12.csv\n",
      "  written dataset 5 to disk\n",
      "  analyzed test dataset 5\n",
      "... processed source dataset 5: 12.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/12.test.csv\n",
      "> Processing source dataset 6: 1.csv\n",
      "  written dataset 6 to disk\n",
      "  analyzed test dataset 6\n",
      "... processed source dataset 6: 1.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/1.test.csv\n",
      "> Processing source dataset 7: 01.csv\n",
      "  written dataset 7 to disk\n",
      "  analyzed test dataset 7\n",
      "... processed source dataset 7: 01.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/01.test.csv\n",
      "> Processing source dataset 8: 0123.csv\n",
      "  written dataset 8 to disk\n",
      "  analyzed test dataset 8\n",
      "... processed source dataset 8: 0123.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/0123.test.csv\n",
      "> Processing source dataset 9: 123.csv\n",
      "  written dataset 9 to disk\n",
      "  analyzed test dataset 9\n",
      "... processed source dataset 9: 123.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/123.test.csv\n",
      "> Processing source dataset 10: 234.csv\n",
      "  written dataset 10 to disk\n",
      "  analyzed test dataset 10\n",
      "... processed source dataset 10: 234.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/234.test.csv\n",
      "> Processing source dataset 11: 3.csv\n",
      "  written dataset 11 to disk\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  analyzed test dataset 11\n",
      "... processed source dataset 11: 3.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/3.test.csv\n",
      "> Processing source dataset 12: 1234.csv\n",
      "  written dataset 12 to disk\n",
      "  analyzed test dataset 12\n",
      "... processed source dataset 12: 1234.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/1234.test.csv\n",
      "> Processing source dataset 13: 23.csv\n",
      "  written dataset 13 to disk\n",
      "  analyzed test dataset 13\n",
      "... processed source dataset 13: 23.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/23.test.csv\n",
      "> Processing source dataset 14: 0.csv\n",
      "  written dataset 14 to disk\n",
      "  analyzed test dataset 14\n",
      "... processed source dataset 14: 0.csv -> /home/projects/akita/data/benchmark-data/data-processed/multivariate/TODS-synthetic/0.test.csv\n"
     ]
    }
   ],
   "source": [
    "for input_type in (\"univariate\", \"multivariate\"):\n",
    "\n",
    "    # create target directory\n",
    "    dataset_subfolder = Path(input_type) / dataset_collection_name\n",
    "    target_subfolder = target_folder / dataset_subfolder\n",
    "    target_subfolder.mkdir(parents=True, exist_ok=True)\n",
    "    print(f\"Created directories {target_subfolder}\")\n",
    "\n",
    "    for i, file in enumerate(list_datasets(input_type)):\n",
    "        print(f\"> Processing source dataset {i}: {file.name}\")\n",
    "        dataset_name = file.stem\n",
    "        test_filename = f\"{dataset_name}.test.csv\"\n",
    "        test_path = dataset_subfolder / test_filename\n",
    "        target_test_filepath = target_subfolder / test_filename\n",
    "        target_meta_filepath = target_test_filepath.parent / f\"{dataset_name}.{Datasets.METADATA_FILENAME_SUFFIX}\"\n",
    "\n",
    "        # Prepare datasets\n",
    "        if not target_test_filepath.exists() or not target_meta_filepath.exists():\n",
    "            df_test = prepare_dataset(file)\n",
    "            df_test.to_csv(target_test_filepath, index=False)\n",
    "            print(f\"  written dataset {i} to disk\")\n",
    "        else:\n",
    "            df_test = None\n",
    "            print(f\"  skipped writing dataset {i} to disk, because it already exists.\")\n",
    "\n",
    "        # Prepare metadata\n",
    "        def analyze(df_test):\n",
    "            da = DatasetAnalyzer((dataset_collection_name, dataset_name), is_train=False, df=df_test)\n",
    "            da.save_to_json(target_meta_filepath, overwrite=True)\n",
    "            meta = da.metadata\n",
    "            print(f\"  analyzed test dataset {i}\")\n",
    "            return meta\n",
    "\n",
    "        if target_meta_filepath.exists():\n",
    "            try:\n",
    "                meta = DatasetAnalyzer.load_from_json(target_meta_filepath, train=False)\n",
    "            except ValueError:\n",
    "                if df_test is None:\n",
    "                    df_test = pd.read_csv(target_test_filepath)\n",
    "                meta = analyze(df_test)\n",
    "        else:\n",
    "            meta = analyze(df_test)\n",
    "\n",
    "        dm.add_dataset(DatasetRecord(\n",
    "              collection_name=dataset_collection_name,\n",
    "              dataset_name=dataset_name,\n",
    "              train_path=None,\n",
    "              test_path=test_path,\n",
    "              dataset_type=dataset_type,\n",
    "              datetime_index=datetime_index,\n",
    "              split_at=split_at,\n",
    "              train_type=train_type,\n",
    "              train_is_normal=train_is_normal,\n",
    "              input_type=input_type,\n",
    "              length=meta.length,\n",
    "              dimensions=meta.dimensions,\n",
    "              contamination=meta.contamination,\n",
    "              num_anomalies=meta.num_anomalies,\n",
    "              min_anomaly_length=meta.anomaly_length.min,\n",
    "              median_anomaly_length=meta.anomaly_length.median,\n",
    "              max_anomaly_length=meta.anomaly_length.max,\n",
    "              mean=meta.mean,\n",
    "              stddev=meta.stddev,\n",
    "              trend=meta.trend,\n",
    "              stationarity=meta.get_stationarity_name(),\n",
    "              period_size=np.nan\n",
    "        ))\n",
    "        print(f\"... processed source dataset {i}: {file.name} -> {target_test_filepath}\")\n",
    "\n",
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "      <th>dimensions</th>\n",
       "      <th>contamination</th>\n",
       "      <th>num_anomalies</th>\n",
       "      <th>min_anomaly_length</th>\n",
       "      <th>median_anomaly_length</th>\n",
       "      <th>max_anomaly_length</th>\n",
       "      <th>mean</th>\n",
       "      <th>stddev</th>\n",
       "      <th>trend</th>\n",
       "      <th>stationarity</th>\n",
       "      <th>period_size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"35\" valign=\"top\">TODS-synthetic</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/0.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.002551</td>\n",
       "      <td>1.366525</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/01.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0975</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.001032</td>\n",
       "      <td>1.365839</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>012</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/012.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1425</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000918</td>\n",
       "      <td>1.367133</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0123</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/0123.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1875</td>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.002521</td>\n",
       "      <td>1.369251</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>01234</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/01234.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.2100</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>-1.250979</td>\n",
       "      <td>2.007675</td>\n",
       "      <td>linear trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/1.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.001928</td>\n",
       "      <td>1.351643</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/12.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0950</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>0.005766</td>\n",
       "      <td>1.348473</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/123.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1450</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.015468</td>\n",
       "      <td>1.346421</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/1234.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>-0.029218</td>\n",
       "      <td>1.365900</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/2.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000113</td>\n",
       "      <td>1.349680</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/23.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.000468</td>\n",
       "      <td>1.351717</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>234</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/234.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.1500</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>-1.358532</td>\n",
       "      <td>1.872189</td>\n",
       "      <td>linear trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/3.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.006252</td>\n",
       "      <td>1.348045</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/34.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0925</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>0.022752</td>\n",
       "      <td>1.379023</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>multivariate/TODS-synthetic/4.test.csv</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>multivariate</td>\n",
       "      <td>400</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.348701</td>\n",
       "      <td>1.622764</td>\n",
       "      <td>no trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_global_0.05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_global_0....</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.005149</td>\n",
       "      <td>1.069512</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_global_0.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_global_0....</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.007318</td>\n",
       "      <td>1.072482</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_global_0.15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_global_0....</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0.002294</td>\n",
       "      <td>1.076513</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_global_0.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_global_0....</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>0.003915</td>\n",
       "      <td>1.087508</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_seasonal_0.05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_seasonal_...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.056168</td>\n",
       "      <td>1.062415</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_seasonal_0.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_seasonal_...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>0.009537</td>\n",
       "      <td>1.061707</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_seasonal_0.15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_seasonal_...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0.003050</td>\n",
       "      <td>1.071799</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_seasonal_0.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_seasonal_...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>0.033092</td>\n",
       "      <td>1.072643</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_trend_0.05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_trend_0.0...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>-0.714427</td>\n",
       "      <td>2.017109</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_trend_0.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_trend_0.1...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>-1.366927</td>\n",
       "      <td>2.259004</td>\n",
       "      <td>no trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>53.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_trend_0.15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_trend_0.1...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1200</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>0.823073</td>\n",
       "      <td>1.895729</td>\n",
       "      <td>no trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collective_trend_0.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/collective_trend_0.2...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1700</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>0.673073</td>\n",
       "      <td>5.115071</td>\n",
       "      <td>kubic trend</td>\n",
       "      <td>trend_stationary</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_contextual_0.05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_contextual_0.0...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.017953</td>\n",
       "      <td>1.080364</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_contextual_0.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_contextual_0.1...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0950</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.029123</td>\n",
       "      <td>1.091983</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_contextual_0.15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_contextual_0.1...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.024305</td>\n",
       "      <td>1.089740</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_contextual_0.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_contextual_0.2...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1900</td>\n",
       "      <td>36</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.026026</td>\n",
       "      <td>1.109937</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_global_0.05</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_global_0.05.te...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0500</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>-0.019453</td>\n",
       "      <td>1.312914</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_global_0.1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_global_0.1.tes...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0900</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.053906</td>\n",
       "      <td>1.480799</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_global_0.15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_global_0.15.te...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-0.013920</td>\n",
       "      <td>1.656877</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>point_global_0.2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/TODS-synthetic/point_global_0.2.tes...</td>\n",
       "      <td>synthetic</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>0.1850</td>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0.026570</td>\n",
       "      <td>1.830920</td>\n",
       "      <td>no trend</td>\n",
       "      <td>difference_stationary</td>\n",
       "      <td>25.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         train_path  \\\n",
       "collection_name dataset_name                          \n",
       "TODS-synthetic  0                               NaN   \n",
       "                01                              NaN   \n",
       "                012                             NaN   \n",
       "                0123                            NaN   \n",
       "                01234                           NaN   \n",
       "                1                               NaN   \n",
       "                12                              NaN   \n",
       "                123                             NaN   \n",
       "                1234                            NaN   \n",
       "                2                               NaN   \n",
       "                23                              NaN   \n",
       "                234                             NaN   \n",
       "                3                               NaN   \n",
       "                34                              NaN   \n",
       "                4                               NaN   \n",
       "                collective_global_0.05          NaN   \n",
       "                collective_global_0.1           NaN   \n",
       "                collective_global_0.15          NaN   \n",
       "                collective_global_0.2           NaN   \n",
       "                collective_seasonal_0.05        NaN   \n",
       "                collective_seasonal_0.1         NaN   \n",
       "                collective_seasonal_0.15        NaN   \n",
       "                collective_seasonal_0.2         NaN   \n",
       "                collective_trend_0.05           NaN   \n",
       "                collective_trend_0.1            NaN   \n",
       "                collective_trend_0.15           NaN   \n",
       "                collective_trend_0.2            NaN   \n",
       "                point_contextual_0.05           NaN   \n",
       "                point_contextual_0.1            NaN   \n",
       "                point_contextual_0.15           NaN   \n",
       "                point_contextual_0.2            NaN   \n",
       "                point_global_0.05               NaN   \n",
       "                point_global_0.1                NaN   \n",
       "                point_global_0.15               NaN   \n",
       "                point_global_0.2                NaN   \n",
       "\n",
       "                                                                                  test_path  \\\n",
       "collection_name dataset_name                                                                  \n",
       "TODS-synthetic  0                                    multivariate/TODS-synthetic/0.test.csv   \n",
       "                01                                  multivariate/TODS-synthetic/01.test.csv   \n",
       "                012                                multivariate/TODS-synthetic/012.test.csv   \n",
       "                0123                              multivariate/TODS-synthetic/0123.test.csv   \n",
       "                01234                            multivariate/TODS-synthetic/01234.test.csv   \n",
       "                1                                    multivariate/TODS-synthetic/1.test.csv   \n",
       "                12                                  multivariate/TODS-synthetic/12.test.csv   \n",
       "                123                                multivariate/TODS-synthetic/123.test.csv   \n",
       "                1234                              multivariate/TODS-synthetic/1234.test.csv   \n",
       "                2                                    multivariate/TODS-synthetic/2.test.csv   \n",
       "                23                                  multivariate/TODS-synthetic/23.test.csv   \n",
       "                234                                multivariate/TODS-synthetic/234.test.csv   \n",
       "                3                                    multivariate/TODS-synthetic/3.test.csv   \n",
       "                34                                  multivariate/TODS-synthetic/34.test.csv   \n",
       "                4                                    multivariate/TODS-synthetic/4.test.csv   \n",
       "                collective_global_0.05    univariate/TODS-synthetic/collective_global_0....   \n",
       "                collective_global_0.1     univariate/TODS-synthetic/collective_global_0....   \n",
       "                collective_global_0.15    univariate/TODS-synthetic/collective_global_0....   \n",
       "                collective_global_0.2     univariate/TODS-synthetic/collective_global_0....   \n",
       "                collective_seasonal_0.05  univariate/TODS-synthetic/collective_seasonal_...   \n",
       "                collective_seasonal_0.1   univariate/TODS-synthetic/collective_seasonal_...   \n",
       "                collective_seasonal_0.15  univariate/TODS-synthetic/collective_seasonal_...   \n",
       "                collective_seasonal_0.2   univariate/TODS-synthetic/collective_seasonal_...   \n",
       "                collective_trend_0.05     univariate/TODS-synthetic/collective_trend_0.0...   \n",
       "                collective_trend_0.1      univariate/TODS-synthetic/collective_trend_0.1...   \n",
       "                collective_trend_0.15     univariate/TODS-synthetic/collective_trend_0.1...   \n",
       "                collective_trend_0.2      univariate/TODS-synthetic/collective_trend_0.2...   \n",
       "                point_contextual_0.05     univariate/TODS-synthetic/point_contextual_0.0...   \n",
       "                point_contextual_0.1      univariate/TODS-synthetic/point_contextual_0.1...   \n",
       "                point_contextual_0.15     univariate/TODS-synthetic/point_contextual_0.1...   \n",
       "                point_contextual_0.2      univariate/TODS-synthetic/point_contextual_0.2...   \n",
       "                point_global_0.05         univariate/TODS-synthetic/point_global_0.05.te...   \n",
       "                point_global_0.1          univariate/TODS-synthetic/point_global_0.1.tes...   \n",
       "                point_global_0.15         univariate/TODS-synthetic/point_global_0.15.te...   \n",
       "                point_global_0.2          univariate/TODS-synthetic/point_global_0.2.tes...   \n",
       "\n",
       "                                         dataset_type  datetime_index  \\\n",
       "collection_name dataset_name                                            \n",
       "TODS-synthetic  0                           synthetic           False   \n",
       "                01                          synthetic           False   \n",
       "                012                         synthetic           False   \n",
       "                0123                        synthetic           False   \n",
       "                01234                       synthetic           False   \n",
       "                1                           synthetic           False   \n",
       "                12                          synthetic           False   \n",
       "                123                         synthetic           False   \n",
       "                1234                        synthetic           False   \n",
       "                2                           synthetic           False   \n",
       "                23                          synthetic           False   \n",
       "                234                         synthetic           False   \n",
       "                3                           synthetic           False   \n",
       "                34                          synthetic           False   \n",
       "                4                           synthetic           False   \n",
       "                collective_global_0.05      synthetic           False   \n",
       "                collective_global_0.1       synthetic           False   \n",
       "                collective_global_0.15      synthetic           False   \n",
       "                collective_global_0.2       synthetic           False   \n",
       "                collective_seasonal_0.05    synthetic           False   \n",
       "                collective_seasonal_0.1     synthetic           False   \n",
       "                collective_seasonal_0.15    synthetic           False   \n",
       "                collective_seasonal_0.2     synthetic           False   \n",
       "                collective_trend_0.05       synthetic           False   \n",
       "                collective_trend_0.1        synthetic           False   \n",
       "                collective_trend_0.15       synthetic           False   \n",
       "                collective_trend_0.2        synthetic           False   \n",
       "                point_contextual_0.05       synthetic           False   \n",
       "                point_contextual_0.1        synthetic           False   \n",
       "                point_contextual_0.15       synthetic           False   \n",
       "                point_contextual_0.2        synthetic           False   \n",
       "                point_global_0.05           synthetic           False   \n",
       "                point_global_0.1            synthetic           False   \n",
       "                point_global_0.15           synthetic           False   \n",
       "                point_global_0.2            synthetic           False   \n",
       "\n",
       "                                          split_at    train_type  \\\n",
       "collection_name dataset_name                                       \n",
       "TODS-synthetic  0                              NaN  unsupervised   \n",
       "                01                             NaN  unsupervised   \n",
       "                012                            NaN  unsupervised   \n",
       "                0123                           NaN  unsupervised   \n",
       "                01234                          NaN  unsupervised   \n",
       "                1                              NaN  unsupervised   \n",
       "                12                             NaN  unsupervised   \n",
       "                123                            NaN  unsupervised   \n",
       "                1234                           NaN  unsupervised   \n",
       "                2                              NaN  unsupervised   \n",
       "                23                             NaN  unsupervised   \n",
       "                234                            NaN  unsupervised   \n",
       "                3                              NaN  unsupervised   \n",
       "                34                             NaN  unsupervised   \n",
       "                4                              NaN  unsupervised   \n",
       "                collective_global_0.05         NaN  unsupervised   \n",
       "                collective_global_0.1          NaN  unsupervised   \n",
       "                collective_global_0.15         NaN  unsupervised   \n",
       "                collective_global_0.2          NaN  unsupervised   \n",
       "                collective_seasonal_0.05       NaN  unsupervised   \n",
       "                collective_seasonal_0.1        NaN  unsupervised   \n",
       "                collective_seasonal_0.15       NaN  unsupervised   \n",
       "                collective_seasonal_0.2        NaN  unsupervised   \n",
       "                collective_trend_0.05          NaN  unsupervised   \n",
       "                collective_trend_0.1           NaN  unsupervised   \n",
       "                collective_trend_0.15          NaN  unsupervised   \n",
       "                collective_trend_0.2           NaN  unsupervised   \n",
       "                point_contextual_0.05          NaN  unsupervised   \n",
       "                point_contextual_0.1           NaN  unsupervised   \n",
       "                point_contextual_0.15          NaN  unsupervised   \n",
       "                point_contextual_0.2           NaN  unsupervised   \n",
       "                point_global_0.05              NaN  unsupervised   \n",
       "                point_global_0.1               NaN  unsupervised   \n",
       "                point_global_0.15              NaN  unsupervised   \n",
       "                point_global_0.2               NaN  unsupervised   \n",
       "\n",
       "                                          train_is_normal    input_type  \\\n",
       "collection_name dataset_name                                              \n",
       "TODS-synthetic  0                                   False  multivariate   \n",
       "                01                                  False  multivariate   \n",
       "                012                                 False  multivariate   \n",
       "                0123                                False  multivariate   \n",
       "                01234                               False  multivariate   \n",
       "                1                                   False  multivariate   \n",
       "                12                                  False  multivariate   \n",
       "                123                                 False  multivariate   \n",
       "                1234                                False  multivariate   \n",
       "                2                                   False  multivariate   \n",
       "                23                                  False  multivariate   \n",
       "                234                                 False  multivariate   \n",
       "                3                                   False  multivariate   \n",
       "                34                                  False  multivariate   \n",
       "                4                                   False  multivariate   \n",
       "                collective_global_0.05              False    univariate   \n",
       "                collective_global_0.1               False    univariate   \n",
       "                collective_global_0.15              False    univariate   \n",
       "                collective_global_0.2               False    univariate   \n",
       "                collective_seasonal_0.05            False    univariate   \n",
       "                collective_seasonal_0.1             False    univariate   \n",
       "                collective_seasonal_0.15            False    univariate   \n",
       "                collective_seasonal_0.2             False    univariate   \n",
       "                collective_trend_0.05               False    univariate   \n",
       "                collective_trend_0.1                False    univariate   \n",
       "                collective_trend_0.15               False    univariate   \n",
       "                collective_trend_0.2                False    univariate   \n",
       "                point_contextual_0.05               False    univariate   \n",
       "                point_contextual_0.1                False    univariate   \n",
       "                point_contextual_0.15               False    univariate   \n",
       "                point_contextual_0.2                False    univariate   \n",
       "                point_global_0.05                   False    univariate   \n",
       "                point_global_0.1                    False    univariate   \n",
       "                point_global_0.15                   False    univariate   \n",
       "                point_global_0.2                    False    univariate   \n",
       "\n",
       "                                          length  dimensions  contamination  \\\n",
       "collection_name dataset_name                                                  \n",
       "TODS-synthetic  0                            400           5         0.0500   \n",
       "                01                           400           5         0.0975   \n",
       "                012                          400           5         0.1425   \n",
       "                0123                         400           5         0.1875   \n",
       "                01234                        400           5         0.2100   \n",
       "                1                            400           5         0.0500   \n",
       "                12                           400           5         0.0950   \n",
       "                123                          400           5         0.1450   \n",
       "                1234                         400           5         0.1700   \n",
       "                2                            400           5         0.0500   \n",
       "                23                           400           5         0.1000   \n",
       "                234                          400           5         0.1500   \n",
       "                3                            400           5         0.0500   \n",
       "                34                           400           5         0.0925   \n",
       "                4                            400           5         0.0500   \n",
       "                collective_global_0.05       200           1         0.0500   \n",
       "                collective_global_0.1        200           1         0.1000   \n",
       "                collective_global_0.15       200           1         0.1200   \n",
       "                collective_global_0.2        200           1         0.1700   \n",
       "                collective_seasonal_0.05     200           1         0.0500   \n",
       "                collective_seasonal_0.1      200           1         0.1000   \n",
       "                collective_seasonal_0.15     200           1         0.1200   \n",
       "                collective_seasonal_0.2      200           1         0.1700   \n",
       "                collective_trend_0.05        200           1         0.0500   \n",
       "                collective_trend_0.1         200           1         0.1000   \n",
       "                collective_trend_0.15        200           1         0.1200   \n",
       "                collective_trend_0.2         200           1         0.1700   \n",
       "                point_contextual_0.05        200           1         0.0500   \n",
       "                point_contextual_0.1         200           1         0.0950   \n",
       "                point_contextual_0.15        200           1         0.1400   \n",
       "                point_contextual_0.2         200           1         0.1900   \n",
       "                point_global_0.05            200           1         0.0500   \n",
       "                point_global_0.1             200           1         0.0900   \n",
       "                point_global_0.15            200           1         0.1400   \n",
       "                point_global_0.2             200           1         0.1850   \n",
       "\n",
       "                                          num_anomalies  min_anomaly_length  \\\n",
       "collection_name dataset_name                                                  \n",
       "TODS-synthetic  0                                    20                   1   \n",
       "                01                                   37                   1   \n",
       "                012                                  37                   1   \n",
       "                0123                                 37                   1   \n",
       "                01234                                34                   1   \n",
       "                1                                    20                   1   \n",
       "                12                                   20                   1   \n",
       "                123                                  22                   1   \n",
       "                1234                                 22                   1   \n",
       "                2                                     2                  10   \n",
       "                23                                    4                  10   \n",
       "                234                                   6                  10   \n",
       "                3                                     2                  10   \n",
       "                34                                    3                  10   \n",
       "                4                                     2                  10   \n",
       "                collective_global_0.05                1                  10   \n",
       "                collective_global_0.1                 2                  10   \n",
       "                collective_global_0.15                2                  10   \n",
       "                collective_global_0.2                 3                  10   \n",
       "                collective_seasonal_0.05              1                  10   \n",
       "                collective_seasonal_0.1               2                  10   \n",
       "                collective_seasonal_0.15              2                  10   \n",
       "                collective_seasonal_0.2               3                  10   \n",
       "                collective_trend_0.05                 1                  10   \n",
       "                collective_trend_0.1                  2                  10   \n",
       "                collective_trend_0.15                 2                  10   \n",
       "                collective_trend_0.2                  3                  10   \n",
       "                point_contextual_0.05                10                   1   \n",
       "                point_contextual_0.1                 19                   1   \n",
       "                point_contextual_0.15                28                   1   \n",
       "                point_contextual_0.2                 36                   1   \n",
       "                point_global_0.05                    10                   1   \n",
       "                point_global_0.1                     15                   1   \n",
       "                point_global_0.15                    25                   1   \n",
       "                point_global_0.2                     30                   1   \n",
       "\n",
       "                                          median_anomaly_length  \\\n",
       "collection_name dataset_name                                      \n",
       "TODS-synthetic  0                                             1   \n",
       "                01                                            1   \n",
       "                012                                           1   \n",
       "                0123                                          1   \n",
       "                01234                                         1   \n",
       "                1                                             1   \n",
       "                12                                            1   \n",
       "                123                                           1   \n",
       "                1234                                          1   \n",
       "                2                                            10   \n",
       "                23                                           10   \n",
       "                234                                          10   \n",
       "                3                                            10   \n",
       "                34                                           10   \n",
       "                4                                            10   \n",
       "                collective_global_0.05                       10   \n",
       "                collective_global_0.1                        10   \n",
       "                collective_global_0.15                       12   \n",
       "                collective_global_0.2                        10   \n",
       "                collective_seasonal_0.05                     10   \n",
       "                collective_seasonal_0.1                      10   \n",
       "                collective_seasonal_0.15                     12   \n",
       "                collective_seasonal_0.2                      10   \n",
       "                collective_trend_0.05                        10   \n",
       "                collective_trend_0.1                         10   \n",
       "                collective_trend_0.15                        12   \n",
       "                collective_trend_0.2                         10   \n",
       "                point_contextual_0.05                         1   \n",
       "                point_contextual_0.1                          1   \n",
       "                point_contextual_0.15                         1   \n",
       "                point_contextual_0.2                          1   \n",
       "                point_global_0.05                             1   \n",
       "                point_global_0.1                              1   \n",
       "                point_global_0.15                             1   \n",
       "                point_global_0.2                              1   \n",
       "\n",
       "                                          max_anomaly_length      mean  \\\n",
       "collection_name dataset_name                                             \n",
       "TODS-synthetic  0                                          1  0.002551   \n",
       "                01                                         2  0.001032   \n",
       "                012                                       10  0.000918   \n",
       "                0123                                      10  0.002521   \n",
       "                01234                                     14 -1.250979   \n",
       "                1                                          1 -0.001928   \n",
       "                12                                        10  0.005766   \n",
       "                123                                       10 -0.015468   \n",
       "                1234                                      20 -0.029218   \n",
       "                2                                         10  0.000113   \n",
       "                23                                        10  0.000468   \n",
       "                234                                       10 -1.358532   \n",
       "                3                                         10  0.006252   \n",
       "                34                                        17  0.022752   \n",
       "                4                                         10  0.348701   \n",
       "                collective_global_0.05                    10  0.005149   \n",
       "                collective_global_0.1                     10  0.007318   \n",
       "                collective_global_0.15                    14  0.002294   \n",
       "                collective_global_0.2                     14  0.003915   \n",
       "                collective_seasonal_0.05                  10  0.056168   \n",
       "                collective_seasonal_0.1                   10  0.009537   \n",
       "                collective_seasonal_0.15                  14  0.003050   \n",
       "                collective_seasonal_0.2                   14  0.033092   \n",
       "                collective_trend_0.05                     10 -0.714427   \n",
       "                collective_trend_0.1                      10 -1.366927   \n",
       "                collective_trend_0.15                     14  0.823073   \n",
       "                collective_trend_0.2                      14  0.673073   \n",
       "                point_contextual_0.05                      1  0.017953   \n",
       "                point_contextual_0.1                       1  0.029123   \n",
       "                point_contextual_0.15                      1  0.024305   \n",
       "                point_contextual_0.2                       2  0.026026   \n",
       "                point_global_0.05                          1 -0.019453   \n",
       "                point_global_0.1                           3 -0.053906   \n",
       "                point_global_0.15                          3 -0.013920   \n",
       "                point_global_0.2                           3  0.026570   \n",
       "\n",
       "                                            stddev         trend  \\\n",
       "collection_name dataset_name                                       \n",
       "TODS-synthetic  0                         1.366525      no trend   \n",
       "                01                        1.365839      no trend   \n",
       "                012                       1.367133      no trend   \n",
       "                0123                      1.369251      no trend   \n",
       "                01234                     2.007675  linear trend   \n",
       "                1                         1.351643      no trend   \n",
       "                12                        1.348473      no trend   \n",
       "                123                       1.346421      no trend   \n",
       "                1234                      1.365900      no trend   \n",
       "                2                         1.349680      no trend   \n",
       "                23                        1.351717      no trend   \n",
       "                234                       1.872189  linear trend   \n",
       "                3                         1.348045      no trend   \n",
       "                34                        1.379023      no trend   \n",
       "                4                         1.622764      no trend   \n",
       "                collective_global_0.05    1.069512      no trend   \n",
       "                collective_global_0.1     1.072482      no trend   \n",
       "                collective_global_0.15    1.076513      no trend   \n",
       "                collective_global_0.2     1.087508      no trend   \n",
       "                collective_seasonal_0.05  1.062415      no trend   \n",
       "                collective_seasonal_0.1   1.061707      no trend   \n",
       "                collective_seasonal_0.15  1.071799      no trend   \n",
       "                collective_seasonal_0.2   1.072643      no trend   \n",
       "                collective_trend_0.05     2.017109   kubic trend   \n",
       "                collective_trend_0.1      2.259004      no trend   \n",
       "                collective_trend_0.15     1.895729      no trend   \n",
       "                collective_trend_0.2      5.115071   kubic trend   \n",
       "                point_contextual_0.05     1.080364      no trend   \n",
       "                point_contextual_0.1      1.091983      no trend   \n",
       "                point_contextual_0.15     1.089740      no trend   \n",
       "                point_contextual_0.2      1.109937      no trend   \n",
       "                point_global_0.05         1.312914      no trend   \n",
       "                point_global_0.1          1.480799      no trend   \n",
       "                point_global_0.15         1.656877      no trend   \n",
       "                point_global_0.2          1.830920      no trend   \n",
       "\n",
       "                                                   stationarity  period_size  \n",
       "collection_name dataset_name                                                  \n",
       "TODS-synthetic  0                         difference_stationary         25.0  \n",
       "                01                        difference_stationary         25.0  \n",
       "                012                       difference_stationary         25.0  \n",
       "                0123                      difference_stationary         25.0  \n",
       "                01234                          trend_stationary         25.0  \n",
       "                1                         difference_stationary         25.0  \n",
       "                12                        difference_stationary         25.0  \n",
       "                123                       difference_stationary         25.0  \n",
       "                1234                      difference_stationary         25.0  \n",
       "                2                         difference_stationary         25.0  \n",
       "                23                        difference_stationary         25.0  \n",
       "                234                            trend_stationary         25.0  \n",
       "                3                         difference_stationary         25.0  \n",
       "                34                        difference_stationary         25.0  \n",
       "                4                              trend_stationary         25.0  \n",
       "                collective_global_0.05    difference_stationary         25.0  \n",
       "                collective_global_0.1     difference_stationary         25.0  \n",
       "                collective_global_0.15    difference_stationary         25.0  \n",
       "                collective_global_0.2     difference_stationary         25.0  \n",
       "                collective_seasonal_0.05  difference_stationary         24.0  \n",
       "                collective_seasonal_0.1   difference_stationary         24.0  \n",
       "                collective_seasonal_0.15  difference_stationary         24.0  \n",
       "                collective_seasonal_0.2   difference_stationary         25.0  \n",
       "                collective_trend_0.05          trend_stationary         25.0  \n",
       "                collective_trend_0.1           trend_stationary         53.0  \n",
       "                collective_trend_0.15          trend_stationary         24.0  \n",
       "                collective_trend_0.2           trend_stationary          1.0  \n",
       "                point_contextual_0.05     difference_stationary         25.0  \n",
       "                point_contextual_0.1      difference_stationary         25.0  \n",
       "                point_contextual_0.15     difference_stationary         25.0  \n",
       "                point_contextual_0.2      difference_stationary         25.0  \n",
       "                point_global_0.05         difference_stationary         25.0  \n",
       "                point_global_0.1          difference_stationary         25.0  \n",
       "                point_global_0.15         difference_stationary         25.0  \n",
       "                point_global_0.2          difference_stationary         25.0  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name,dataset_collection_name), slice(None))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/012.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/2.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/4.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/34.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/01234.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/12.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/1.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/01.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/0123.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/123.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/234.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/3.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/1234.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/23.csv'),\n",
       " PosixPath('/home/projects/akita/data/benchmark-data/data-raw/TODS-synthetic/multidataset/0.csv')]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "uni_datasets = list_datasets(\"univariate\")\n",
    "multi_datasets = list_datasets(\"multivariate\")\n",
    "multi_datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>anomaly</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.131232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.398736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.809108</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.007888</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.340091</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1.377897</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-1.279601</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.950526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.767629</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.254822</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        value  anomaly\n",
       "0   -0.131232        0\n",
       "1    0.398736        0\n",
       "2    0.809108        0\n",
       "3    1.007888        0\n",
       "4    1.340091        0\n",
       "..        ...      ...\n",
       "195 -1.377897        0\n",
       "196 -1.279601        0\n",
       "197 -0.950526        0\n",
       "198 -0.767629        0\n",
       "199 -0.254822        0\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(uni_datasets[0])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>is_anomaly</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.131232</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.398736</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.809108</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.007888</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.340091</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>-1.377897</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>-1.279601</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>-0.950526</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>-0.767629</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>-0.254822</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              value  is_anomaly\n",
       "timestamp                      \n",
       "0         -0.131232           0\n",
       "1          0.398736           0\n",
       "2          0.809108           0\n",
       "3          1.007888           0\n",
       "4          1.340091           0\n",
       "...             ...         ...\n",
       "195       -1.377897           0\n",
       "196       -1.279601           0\n",
       "197       -0.950526           0\n",
       "198       -0.767629           0\n",
       "199       -0.254822           0\n",
       "\n",
       "[200 rows x 2 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index.name = \"timestamp\"\n",
    "df = df.rename(columns={\"anomaly\": \"is_anomaly\"})\n",
    "df = df.reset_index()\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
